Cargando…

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment an...

Descripción completa

Detalles Bibliográficos
Autores principales:	Mukhiddinov, Mukhriddin, Abdusalomov, Akmalbek Bobomirzaevich, Cho, Jinsoo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740073/ https://www.ncbi.nlm.nih.gov/pubmed/36502081 http://dx.doi.org/10.3390/s22239384

Ejemplares similares

Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired
por: Mukhiddinov, Mukhriddin, et al.
Publicado: (2022)

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images
por: Saydirasulovich, Saydirasulov Norkobil, et al.
Publicado: (2023)

Parking Lot Occupancy Detection with Improved MobileNetV3
por: Yuldashev, Yusufbek, et al.
Publicado: (2023)

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging
por: Abdusalomov, Akmalbek Bobomirzaevich, et al.
Publicado: (2023)

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach
por: Mamieva, Dilnoza, et al.
Publicado: (2023)

Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces
por: Farkhod, Akhmedov, et al.
Publicado: (2022)

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People
por: Abdusalomov, Akmalbek Bobomirzaevich, et al.
Publicado: (2022)

An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach
por: Abdusalomov, Akmalbek Bobomirzaevich, et al.
Publicado: (2023)

Oriented Vehicle Detection in Aerial Images Based on YOLOv4
por: Lin, Tai-Hung, et al.
Publicado: (2022)

RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection
por: Jiang, Yutian, et al.
Publicado: (2023)

An Optimal Routing Algorithm for Unmanned Aerial Vehicles
por: Kim, Sooyeon, et al.
Publicado: (2021)

An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems
por: Abdusalomov, Akmalbek, et al.
Publicado: (2021)

Multispectral Detection of Commercial Unmanned Aerial Vehicles
por: Farlik, Jan, et al.
Publicado: (2019)

Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images
por: Kim, Soon-Young, et al.
Publicado: (2023)

Handbook of unmanned aerial vehicles
por: Valavanis, Kimon, et al.
Publicado: (2015)

Guidance of Unmanned Aerial Vehicles
por: Yanushevsky, Rafael
Publicado: (2011)

Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images
por: Nodirov, Jakhongir, et al.
Publicado: (2022)

A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments
por: Norkobil Saydirasulovich, Saydirasulov, et al.
Publicado: (2023)

International Symposium on Unmanned Aerial Vehicles
por: Valavanis, Kimon, et al.
Publicado: (2009)

Unmanned aerial vehicles: embedded control
por: Lozano, R
Publicado: (2010)

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety
por: Safarov, Furkat, et al.
Publicado: (2023)

Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles
por: Song, Jiang, et al.
Publicado: (2022)

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
por: Mukhiddinov, Mukhriddin, et al.
Publicado: (2022)

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors
por: Muminov, Azamjon, et al.
Publicado: (2022)

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches
por: Avazov, Kuldoshbay, et al.
Publicado: (2023)

Simulating unmanned aerial vehicle flight control and collision detection
por: Liu, Mengtian, et al.
Publicado: (2019)

Modeling and Optimization of Multiple Unmanned Aerial Vehicles System Architecture Alternatives
por: Qin, Dongliang, et al.
Publicado: (2014)

An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning
por: Tang, An-Di, et al.
Publicado: (2021)

Bio-inspired computation in unmanned aerial vehicles
por: Duan, Haibin, et al.
Publicado: (2014)

Recent advances in research on unmanned aerial vehicles
por: Fahroo, Fariba, et al.
Publicado: (2013)

Reliability and Maintenance Analysis of Unmanned Aerial Vehicles †
por: Petritoli, Enrico, et al.
Publicado: (2018)

Microphones as Airspeed Sensors for Unmanned Aerial Vehicles
por: Makaveev, Momchil, et al.
Publicado: (2023)

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm
por: Abdusalomov, Akmalbek Bobomirzaevich, et al.
Publicado: (2023)

Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5
por: Li, Shuaicai, et al.
Publicado: (2023)

Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features
por: Mamieva, Dilnoza, et al.
Publicado: (2023)

Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery
por: Han, Seongkyun, et al.
Publicado: (2019)

Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles
por: Lin, Lishan, et al.
Publicado: (2019)

Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles
por: Gökçe, Fatih, et al.
Publicado: (2015)

UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective
por: Liu, Mingjie, et al.
Publicado: (2020)

Unsupervised Fault Detection on Unmanned Aerial Vehicles: Encoding and Thresholding Approach †
por: Park, Kyung Ho, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_a6tr21npka9lt7ugbjrhirfvbu